Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/303178 
Year of Publication: 
2024
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2024-30
Publisher: 
Bank of Canada, Ottawa
Abstract: 
The macroeconomy is a complicated dynamic system with significant uncertainties that make modelling difficult. Consequently, decision-makers consider multiple models that provide different predictions and policy recommendations and then synthesize that information into a policy decision. We use Bayesian predictive decision synthesis (BPDS) as a way formalize this monetary policy decision-making process. BPDS draws on recent developments in model combination and statistical decision theory that make it possible to combine models in a manner that incorporates decision goals, expectations and outcomes. We develop a BPDS procedure for a case study of monetary policy decision-making with an inflation-targeting central bank and compare the results against standard model-combination approaches.
Subjects: 
Econometric and statistical methods
Economic models
Monetary Policy
JEL: 
C11
C32
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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